OpenRaiser/Intern-Atlas huggingface dataset:
Denoising based Vector Approximate Message Passing
Venue: arXiv cs.IT 2016
Year: 2016

The denoising-based approximate message passing (D-AMP) methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D into the AMP algorithm to achieve state-of-the-art compressive image recovery. But AMP diverges with small deviations from the i.i.d.-Gaussian assumption on the measurement matrix. Recently, the vector AMP (VAMP) algorithm has been proposed to fix this problem. In this work, we show that the benefits of VAMP extend to D-VAMP.
Practical scalability assesment for parallel scientific numerical applications
Venue: arXiv cs.DC 2016
Year: 2016

The concept of scalability analysis of numerical parallel applications has been revisited, with the specific goals defined for the performance estimation of research applications. A series of Community Climate Model System (CCSM) numerical simulations were used to test the several MPI implementations, determine optimal use of the system resources, and their scalability. The scaling capacity and model throughput performance metrics for $N$ cores showed a log-linear behavior approximated by a power fit in the form of $C(N)=bN^a$, where $a$ and $b$ are two empirical constants. Different metrics yielded identical power coefficients ($a$), but different dimensionality coefficients ($b$). This model was consistent except for the large numbers of N. The power fit approach appears to be very useful for scalability estimates, especially when no serial testing is possible. Scalability analysis of additional scientific application has been conducted in the similar way to validate the robustness of the power fit approach.
Surviving in Directed Graphs: A Polylogarithmic Approximation for Two-Connected Directed Steiner Tree
Venue: arXiv cs.DS 2016
Year: 2016

In this paper, we study a survivable network design problem on directed graphs, 2-Connected Directed Steiner Tree (2-DST): given an $n$-vertex weighted directed graph, a root $r$, and a set of $h$ terminals $S$, find a min-cost subgraph $H$ that has two edge/vertex disjoint paths from $r$ to any $t\in S$. 2-DST is a natural generalization of the classical Directed Steiner Tree problem (DST), where we have an additional requirement that the network must tolerate one failure. No non-trivial approximation is known for 2-DST. This was left as an open problem by Feldman et al., [SODA'09; JCSS] and has then been studied by Cheriyan et al. [SODA'12; TALG] and Laekhanukit SODA'14]. However, no positive result was known except for the special case of a $D$-shallow instance [Laekhanukit, ICALP'16]. We present an $O(D^3\log D\cdot h^{2/D}\cdot \log n)$ approximation algorithm for 2-DST that runs in time $O(n^{O(D)})$, for any $D\in[\log_2h]$. This implies a polynomial-time $O(h^ε\log n)$ approximation for any constant $ε>0$, and a poly-logarithmic approximation running in quasi-polynomial time. We remark that this is essentially the best-known even for the classical DST, and the latter problem is $O(\log^{2-ε}n)$-hard to approximate [Halperin and Krauthgamer, STOC'03]. As a by product, we obtain an algorithm with the same approximation guarantee for the $2$-Connected Directed Steiner Subgraph problem, where the goal is to find a min-cost subgraph such that every pair of terminals are $2$-edge/vertex connected.
Deciding Graph non-Hamiltonicity via a Closure Algorithm
Venue: arXiv cs.DS 2016
Year: 2016

We present a matching and LP based heuristic algorithm that decides graph non-Hamiltonicity. Each of the $n!$ Hamilton cycles in a complete directed graph on $n+1$ vertices corresponds with each of the $n!$ $n$-permutation matrices $P$, such that $p_{u,i}=1$ if and only if the $i^{th}$ arc in a cycle enters vertex $u$, starting and ending at vertex $n+1$. A graph instance ($G$) is initially coded as exclusion set $E$, whose members are pairs of components of $P$, $\{p_{u,i} ,p_{v,i+1}\}, i=1,n-1$, for each arc $(u,v)$ not in $G$. For each $\{p_{u,i} ,p_{v,i+1}\}\in E$, the set of $P$ satisfying $p_{u,i}=p_{v,i+1}=1$ correspond with a set of cycles not in $G$. Accounting for all arcs not in $G$, $E$ codes precisely the set of cycles not in $G$. A doubly stochastic-like $\mathcal{O}$($n^4$) formulation of the Hamilton cycle decision problem is then constructed. Each $\{p_{u,i} ,p_{v,j}\}$ is coded as variable $q_{u,i,v,j}$ such that the set of integer extrema is the set of all permutations. We model $G$ by setting each $q_{u,i,v,j}=0$ in correspondence with each $\{p_{u,i} ,p_{v,j}\}\in E$ such that for non-Hamiltonian $G$, integer solutions cannot exist. We recognize non-Hamiltonicity by iteratively deducing additional $q_{u,i,v,j}$ that can be set zero and expanding $E$ until the formulation becomes infeasible, in which case we recognize that no integer solutions exists i.e. $G$ is decided non-Hamiltonian. Over 100 non-Hamiltonian graphs (10 through 104 vertices) and 2000 randomized 31 vertex non-Hamiltonian graphs are tested and correctly decided non-Hamiltonian. For Hamiltonian $G$, the complement of $E$ provides information about covers of matchings, perhaps useful in searching for cycles. We also present an example where the algorithm fails to deduce any integral value for any $q_{u,i,v,j}$ i.e. $G$ is undecided.
A Survey on Replica Server Placement Algorithms for Content Delivery Networks
Venue: arXiv cs.NI 2016
Year: 2016

Content Delivery Networks (CDNs) have gained immense popularity over the years. Replica server placement is a key design issue in CDNs. It entails placing replica servers at meticulous locations, such that cost is minimized and Quality of Service (QoS) of end-users is satisfied. Many replica server placement models have been proposed in the literature of traditional CDN. As the CDN architecture is evolving through the adoption of emerging paradigms, such as, cloud computing and Network Functions Virtualization (NFV), new algorithms are being proposed. In this paper, we present a comprehensive survey of replica server placement algorithms in traditional and emerging paradigm based CDNs. We categorize the algorithms and provide a summary of their characteristics. Besides, we identify requirements for an efficient replica server placement algorithm and perform a comparison in the light of the requirements. Finally, we discuss potential avenues for further research in replica server placement in CDNs.
A Survey on 3D CAD model quality assurance and testing tools
Venue: arXiv cs.GR 2016
Year: 2016

A new taxonomy of issues related to CAD model quality is presented, which distinguishes between explicit and procedural models. For each type of model, morphologic, syntactic, and semantic errors are characterized. The taxonomy was validated successfully when used to classify quality testing tools, which are aimed at detecting and repairing data errors that may affect the simplification, interoperability, and reusability of CAD models. The study shows that low semantic level errors that hamper simplification are reasonably covered in explicit representations, although many CAD quality testers are still unaffordable for Small and Medium Enterprises, both in terms of cost and training time. Interoperability has been reasonably solved by standards like STEP AP 203 and AP214, but model reusability is not feasible in explicit representations. Procedural representations are promising, as interactive modeling editors automatically prevent most morphologic errors derived from unsuitable modeling strategies. Interoperability problems between procedural representations are expected to decrease dramatically with STEP AP242. Higher semantic aspects of quality such as assurance of design intent, however, are hardly supported by current CAD quality testers.
Domain Adaptation For Formant Estimation Using Deep Learning
Venue: arXiv cs.CL 2016
Year: 2016

In this paper we present a domain adaptation technique for formant estimation using a deep network. We first train a deep learning network on a small read speech dataset. We then freeze the parameters of the trained network and use several different datasets to train an adaptation layer that makes the obtained network universal in the sense that it works well for a variety of speakers and speech domains with very different characteristics. We evaluated our adapted network on three datasets, each of which has different speaker characteristics and speech styles. The performance of our method compares favorably with alternative methods for formant estimation.
Generative Adversarial Networks as Variational Training of Energy Based Models
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we study deep generative models for effective unsupervised learning. We propose VGAN, which works by minimizing a variational lower bound of the negative log likelihood (NLL) of an energy based model (EBM), where the model density $p(\mathbf{x})$ is approximated by a variational distribution $q(\mathbf{x})$ that is easy to sample from. The training of VGAN takes a two step procedure: given $p(\mathbf{x})$, $q(\mathbf{x})$ is updated to maximize the lower bound; $p(\mathbf{x})$ is then updated one step with samples drawn from $q(\mathbf{x})$ to decrease the lower bound. VGAN is inspired by the generative adversarial networks (GANs), where $p(\mathbf{x})$ corresponds to the discriminator and $q(\mathbf{x})$ corresponds to the generator, but with several notable differences. We hence name our model variational GANs (VGANs). VGAN provides a practical solution to training deep EBMs in high dimensional space, by eliminating the need of MCMC sampling. From this view, we are also able to identify causes to the difficulty of training GANs and propose viable solutions. \footnote{Experimental code is available at https://github.com/Shuangfei/vgan}
Exploring the Pathways of Adaptation an Avatar 3D Animation Procedures and Virtual Reality Arenas in Research of Human Courtship Behaviour and Sexual Reactivity in Psychological Research
Venue: arXiv cs.HC 2016
Year: 2016

There are many reasons for utilising 3D animation and virtual reality in sexuality research. Apart from providing a mean with which to (re)experience certain situations there are four main advantages: a) bespoke animated stimuli can be created and customized, which is especially important when researching paraphilia and sexual preferences, b) stimulus production is less expensive and easier to produce compared to real world stimuli, c) virtual reality allows us to capture data such as physiological reasons to stimuli, that we would not be able to otherwise (without resorting to self-report measures which are especially problematic in this research domain), d) ethical, legal, and health and safety issues are less complex since neither physical nor psychological harm is caused to animated characters allowing for the safe presentation of stimuli involving vulnerable targets. The animation sub-group has been exploring so far several production quality levels and various animation procedures in a number of available software. The aim is to develop static as well as dynamic, interactive sexual stimuli for sexual diagnostic and therapeutic purposes. We are aware of number of ethical issues related to the use of virtual reality in proposed research are analysed in this chapter.
Parameterized counting of trees, forests and matroid bases
Venue: arXiv cs.CC 2016
Year: 2016

We investigate the complexity of counting trees, forests and bases of matroids from a parameterized point of view. It turns out that the problems of computing the number of trees and forests with $k$ edges are $\# W[1]$-hard when parameterized by $k$. Together with the recent algorithm for deterministic matrix truncation by Lokshtanov et al. (ICALP 2015), the hardness result for $k$-forests implies $\# W[1]$-hardness of the problem of counting bases of a matroid when parameterized by rank or nullity, even if the matroid is restricted to be representable over a field of characteristic $2$. We complement this result by pointing out that the problem becomes fixed parameter tractable for matroids represented over a fixed finite field.
Action2Activity: Recognizing Complex Activities from Sensor Data
Venue: arXiv cs.CV 2016
Year: 2016

As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life. Techniques for action recognition from sensor generated data are mature. However, there has been relatively little work on bridging the gap between actions and activities. To this end, this paper presents a novel approach for complex activity recognition comprising of two components. The first component is temporal pattern mining, which provides a mid-level feature representation for activities, encodes temporal relatedness among actions, and captures the intrinsic properties of activities. The second component is adaptive Multi-Task Learning, which captures relatedness among activities and selects discriminant features. Extensive experiments on a real-world dataset demonstrate the effectiveness of our work.
Geodetic Graphs Homeomorphic to a Given Geodetic Graph
Venue: arXiv cs.DM 2016
Year: 2016

This paper describes a new approach to the problem of generating the class of all geodetic graphs homeomorphic to a given geodetic one. An algorithmic procedure is elaborated to carry out a systematic finding of such a class of graphs. As a result, the enumeration of the class of geodetic graphs homeomorphic to certain Moore graphs has been performed.
Inductive decision based Real Time Occupancy detector in University Buildings
Venue: arXiv cs.CY 2016
Year: 2016

The ability to estimate College Campus Occupancy for Classrooms and Labs in real time has become one of the major concerns for various Academicians, authorities and administrators,where still a manual attendance marking system is being followed. Using a low budget multiple sensor setup installed in a college auditorium, the goal is to build a real-time occupancy detector. This paper presents an Inductive real time Decision tree based classifier using multiple sensor dataset to detect occupancy. Using simple feature based thresholds, Reverberation time which comes out to be a novel as well as most distinguishing feature sampled at various frequencies over a given time interval was used to detect the occupancy with an accuracy of %.Addition of various other sensor data, decreased the accuracy of classification results. The detector setup can be used in various college buildings to provide real time centralised occupancy status thus automating the manual attendance system being used.
High-Resolution Semantic Labeling with Convolutional Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Convolutional neural networks (CNNs) have received increasing attention over the last few years. They were initially conceived for image categorization, i.e., the problem of assigning a semantic label to an entire input image. In this paper we address the problem of dense semantic labeling, which consists in assigning a semantic label to every pixel in an image. Since this requires a high spatial accuracy to determine where labels are assigned, categorization CNNs, intended to be highly robust to local deformations, are not directly applicable. By adapting categorization networks, many semantic labeling CNNs have been recently proposed. Our first contribution is an in-depth analysis of these architectures. We establish the desired properties of an ideal semantic labeling CNN, and assess how those methods stand with regard to these properties. We observe that even though they provide competitive results, these CNNs often underexploit properties of semantic labeling that could lead to more effective and efficient architectures. Out of these observations, we then derive a CNN framework specifically adapted to the semantic labeling problem. In addition to learning features at different resolutions, it learns how to combine these features. By integrating local and global information in an efficient and flexible manner, it outperforms previous techniques. We evaluate the proposed framework and compare it with state-of-the-art architectures on public benchmarks of high-resolution aerial image labeling.
Finite-Horizon Throughput Region for Wireless Multi-User Interference Channels
Venue: arXiv cs.IT 2016
Year: 2016

This paper studies a wireless network consisting of multiple transmitter-receiver pairs where interference is treated as noise. Previously, the throughput region of such networks was characterized for either one time slot or an infinite time horizon. We aim to fill the gap by investigating the throughput region for transmissions over a finite time horizon. Unlike the infinite-horizon throughput region, which is simply the convex hull of the throughput region of one time slot, the finite-horizon throughput region is generally non-convex. Instead of directly characterizing all achievable rate-tuples in the finite-horizon throughput region, we propose a metric termed the rate margin, which not only determines whether any given rate-tuple is within the throughput region (i.e., achievable or unachievable), but also tells the amount of scaling that can be done to the given achievable (unachievable) rate-tuple such that the resulting rate-tuple is still within (brought back into) the throughput region. Furthermore, we derive an efficient algorithm to find the rate-achieving policy for any given rate-tuple in the finite-horizon throughput region.
Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks
Venue: arXiv cs.DC 2016
Year: 2016

Cooperative localization in agent networks based on interagent time-of-flight measurements is closely related to synchronization. To leverage this relation, we propose a Bayesian factor graph framework for cooperative simultaneous localization and synchronization (CoSLAS). This framework is suited to mobile agents and time-varying local clock parameters. Building on the CoSLAS factor graph, we develop a distributed (decentralized) belief propagation algorithm for CoSLAS in the practically important case of an affine clock model and asymmetric time stamping. Our algorithm allows for real-time operation and is suitable for a time-varying network connectivity. To achieve high accuracy at reduced complexity and communication cost, the algorithm combines particle implementations with parametric message representations and takes advantage of a conditional independence property. Simulation results demonstrate the good performance of the proposed algorithm in a challenging scenario with time-varying network connectivity.
Co-primary Spectrum Sharing for Inter-operator Device-to-Device Communication
Venue: arXiv cs.NI 2016
Year: 2016

The business potential of device-to-device (D2D) communication including public safety and vehicular communications will be realized only if direct communication between devices subscribed to different mobile operators (OPs) is supported. One possible way to implement inter-operator D2D communication may use the licensed spectrum of the OPs, i.e., OPs agree to share spectrum in a co-primary manner, and inter-operator D2D communication is allocated over spectral resources contributed from both parties. In this paper, we consider a spectrum sharing scenario where a number of OPs construct a spectrum pool dedicated to support inter-operator D2D communication. OPs negotiate in the form of a non-cooperative game about how much spectrum each OP contributes to the spectrum pool. OPs submit proposals to each other in parallel until a consensus is reached. When every OP has a concave utility function on the box-constrained region, we identify the conditions guaranteeing the existence of a unique equilibrium point. We show that the iterative algorithm based on the OP's best response might not converge to the equilibrium point due to myopically overreacting to the response of the other OPs, while the Jacobi-play strategy update algorithm can converge with an appropriate selection of update parameter. Using the Jacobi-play update algorithm, we illustrate that asymmetric OPs contribute an unequal amount of resources to the spectrum pool; However all participating OPs may experience significant performance gains compared to the scheme without spectrum sharing.
EpistAid: Interactive Interface for Document Filtering in Evidence-based Health Care
Venue: arXiv cs.IR 2016
Year: 2016

Evidence-based health care (EBHC) is an important practice of medicine which attempts to provide systematic scientific evidence to answer clinical questions. In this context, Epistemonikos (www.epistemonikos.org) is one of the first and most important online systems in the field, providing an interface that supports users on searching and filtering scientific articles for practicing EBHC. The system nowadays requires a large amount of expert human effort, where close to 500 physicians manually curate articles to be utilized in the platform. In order to scale up the large and continuous amount of data to keep the system updated, we introduce EpistAid, an interactive intelligent interface which supports clinicians in the process of curating documents for Epistemonikos within lists of papers called evidence matrices. We introduce the characteristics, design and algorithms of our solution, as well as a prototype implementation and a case study to show how our solution addresses the information overload problem in this area.
A Big-Data Approach to Handle Many Process Variations: Tensor Recovery and Applications
Venue: arXiv cs.CE 2016
Year: 2016

Fabrication process variations are a major source of yield degradation in the nano-scale design of integrated circuits (IC), microelectromechanical systems (MEMS) and photonic circuits. Stochastic spectral methods are a promising technique to quantify the uncertainties caused by process variations. Despite their superior efficiency over Monte Carlo for many design cases, these algorithms suffer from the curse of dimensionality; i.e., their computational cost grows very fast as the number of random parameters increases. In order to solve this challenging problem, this paper presents a high-dimensional uncertainty quantification algorithm from a big-data perspective. Specifically, we show that the huge number of (e.g., $1.5 \times 10^{27}$) simulation samples in standard stochastic collocation can be reduced to a very small one (e.g., $500$) by exploiting some hidden structures of a high-dimensional data array. This idea is formulated as a tensor recovery problem with sparse and low-rank constraints; and it is solved with an alternating minimization approach. Numerical results show that our approach can simulate efficiently some ICs, as well as MEMS and photonic problems with over 50 independent random parameters, whereas the traditional algorithm can only handle several random parameters.